Measurement of electromagnetic interference in time-domain

Time-domain EMI measurement systems allow measurement time to be reduced by several orders of magnitude. In this paper a novel real-time operating time-domain EMI measurement system is presented. By the use of several analog-to-digital converters the dynamic range requested by the international EMC standards is achieved. A real-time operating digital signal processing unit is presented. The frequency band that is investigated is subdivided into several sub-bands. A novel implementation of the 9 kHz IF filter for the frequency 150 kHz to 1 GHz is presented. By this way the measurement time has been reduced by a factor of 8000 in comparison to conventional EMI receivers. During emission measurements performed with a modelled IF-bandwidth of 9 kHz the noise floor is decreased to −19 dBµV in the average detector mode by the implemented low noise power splitter. Measurements have been performed with the improved measurement system in the frequency range 30 MHz–1 GHz

Impact of remote sensing upon the planning, management and development of water resources, appendix

Lists are presented of water resource agencies from the federal, state, Water Resources Research Institute, university, local, and private sectors. Information is provided on their water resource activities, computers, and models used. For Basic doc., see N75-25263

Impact of remote sensing upon the planning, management, and development of water resources

Principal water resources users were surveyed to determine the impact of remote data streams on hydrologic computer models. Analysis of responses demonstrated that: most water resources effort suitable to remote sensing inputs is conducted through federal agencies or through federally stimulated research; and, most hydrologic models suitable to remote sensing data are federally developed. Computer usage by major water resources users was analyzed to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era

The application of remote sensing to the development and formulation of hydrologic planning models

For abstract, see N76-14576

Nuclear magnetic resonance measurements reveal the origin of the Debye process in monohydroxy alcohols

Monohydroxy alcohols show a structural relaxation and at longer time scales a Debye-type dielectric peak. From spin-lattice relaxation experiments using different nuclear probes an intermediate, slower-than-structural dynamics is identified for n-butanol. Based on these findings and on diffusion measurements, a model of self-restructuring, transient chains is proposed. The model is demonstrated to explain consistently the so far puzzling observations made for this class of hydrogen-bonded glass forming liquids.Comment: 4 pages, 4 figure

Network Power Fault Detection

Network power fault detection. At least one first network device is instructed to temporarily disconnect from a power supply path of a network, and at least one characteristic of the power supply path of the network is measured at a second network device connected to the network while the at least one first network device is temporarily disconnected from the network

Measuring power consumption in an integrated circuit

A method for determining power consumption of a power domain within an integrated circuit is presented. In a first step, a local power supply impedance profile (Z(f)) of this power domain is determined. Subsequently, a local time-resolved power supply voltage (U(t)) is measured while a well-defined periodic activity is executed in power domain. A set of time-domain measured voltage data (U(t)) is thus accumulated and transformed into the frequency domain to yield a voltage spectrum (U(f)). A current spectrum I(t) is calculated from this voltage profile (U(f)) by using the power supply impedance profile Z(f) of this power domain as I(t)=Ff −1{U(f)/Z(f)}. Finally, a time-resolved power consumption spectrum P(t) is determined from measured voltage spectrum U(t)) and calculated current spectrum (I(t)). This power consumption (P(t)) may be compared with a reference (Pref(t)) to verify whether power consumption within power domain matches expectations